- funded by IMI-JU
- 01 March 2011 - 31 August 2016
- 11 EFPIA members
- 10 Academia and 5 SME's
- Total cost € 23,032,609
Pharmacometric Markup Language (PharmML)
XML based format, PharmML for storing and exchange of pharmacometrics models and associated results.
PharmML provides means to encode
- mathematical/statistical models used in pharmacometrics
- trial design and
- modelling steps in ‘tool agnostic’ manner.
Clinical data - NLME models:
The main target of DDMoRe are clinical trials data and the natural choice are the nonlinear mixed effect models (NLME) commonly used for the analysis of longitudinal population data.
Therefore NLME theory is the mathematical backbone of PharmML (REF1/2) determining its structure and scope.
The main estimation approach is the Maximum Likelihood Estimation but full support for hierarchical models and Bayesian inference (definition of priors) is covered as well.
Scope of PharmML:
1. Continuous data models, called structural models, can be defined as a system of ordinary differential equation (ODE), delay differential equations (DDE) and/or algebraic equations.
For encoding of compartmental pharmacokinetic (PK) models one can use alternatively so called 'PK macros’, a system allowing for model formulation without equations.
2. Discrete data models - covered are count, categorical, time-to-event data models. Markov-type dependencies can be defined and examples had been tested in connection with count and categorical models.
3. Parameter model allowing for implementation of virtually any parameter type used in the NLME models, so called Gaussian model type or alternatively those formulated as equation.
4. Covariate model for integrating discrete or continuous covariates. The latter can be transformed, interpolated or their distribution defined using UncertML or our own ontology/knowledge base with more the 80 distributions.
5. Nested hierarchical variability model capable of expressing complex random error structures.
6. Observation model with flexible residual error model supporting untransformed or transformed continuous data.
7. Trial design model allowing for definition of many common design patterns, drug administration types and encoding of experimental data needed for typical simulation or estimation tasks, such as dosing, observations and covariates.
8. Optimal Experimental Design support extends trial design with design spaces on every trial design element.
9. Hierarchical models/Bayesian inference is possible via assignment of distributions (priors) to any model parameter.
10. Modelling steps: specification of how a mathematical model and the associated trial design can be used with typical modelling tasks such as estimation, simulation, design optimisation/evaluation.
Release of PharmML 0.6
Major extensions compared to the first public release, v. 0.2.1 (November 2013), are:
and many other changes/extensions.
Release of PharmML 0.8
New version of PharmML v0.8 is ready to download - with support for (optimal) trial design, Bayesian inference etc. It is a development version meaning no full spec is available. Only a detailed changes document has been released.
Release of PharmML 0.9
New in this version:
PharmML relates to ProbOnto - the Ontology and Knowledge Base of Probability Distributions
ProbOnto 2.0 - www.probonto.org
REF1. Lavielle, M. Mixed Effects Models for the Population Approach Models,
Tasks, Methods & Tools (Chapman & Hall/CRC Biostatistics Series 2014).
REF2. Bonate, P. Pharmacokinetic-Pharmacodynamic Modeling and Simulation.
Springer Science & Business Media, 2011.
REF3. Swat et al. Pharmacometrics Markup Language (PharmML) Language
cation for Version 0.6. Jan 2015. URL:www.pharmml.org/documentation2
REF4. Swat et al. Pharmacometrics Markup Language (PharmML): Opening New
Perspectives for Model Exchange in Drug Development. CPT Pharmacometrics
Syst Pharmacol. 2015 Jun;4(6):316-9.